Technology may be getting smarter but humans have been getting less intelligent since Victorian times, according to a controversial study. The research claims that up until 180 years ago, people were getting smarter thanks to natural selection favouring'survival of the sharpest'. The emergence of farming, cities and government would have made it easier for smarter people to get on in life, have more children and pass on their genes more widely. But that trend has now being reversed, researchers in Brussels claim. Genes driving intelligence have become less common since Victorian times, because advances in medicine and nutrition means people with lower IQs can have more children that survive into adulthood.

US Navy officers are currently testing the world's first drone-killing laser capable of blasting targets with 30kW of power. The $40 million (£30 million) super laser moves at the speed of light and is'more precise than a bullet', according to naval officers. Live-tests of the new super laser have now begun in the Persian Gulf, and officials have predicted that it will be used in combat by 2020. While laser weapons have been a staple in science fiction films for decades, the US military is inching closer to making these a reality. US Navy officers are currently testing the world's first drone-killing laser capable of blasting targets with 30kW of power (pictured) The LaWS, which stands for Laser Weapons System, is currently being tested on board the USS Ponce amphibious transport ship.

An AI system that can understand the difference between beauty and the mundane has been developed by researchers. Scientists trained a neural network to'quantify' the beauty of outdoor spaces and found that natural scenes such as coasts and mountains ranked highly alongside man-made spectacles such as castles and towers. The findings could help policymakers to build more'beautiful' towns and cities, which will help to boost our mental well-being, researchers claimed. An AI system that can understand the difference between beauty and the mundane has been developed by researchers. Pictured is an image of Big Ben, one of the photos picked out as'beautiful' by the researchers' AI To help them quantify beauty, researchers fed an AI with more 200,000 images of scenery from different parts of the UK.

STEVE INSKEEP: Republicans promised for years to repeal and replace the Affordable Care Act. In fact, they said they'd replace it with something better. President Trump says he would now rather just repeal. Trump said that last night after a Senate bill to replace Obamacare collapsed. Two more Republican senators objected to it. And since they were trying to pass it with GOP votes alone, it was assured of failure.

A team of Afghan girls whose plight resounded with the world won a silver medal for "courageous achievement" at an international robotics contest in the United States, with judges praising the group's "can-do attitude". The First Global Challenge event in Washington ended on Tuesday, having attracted teams of teenagers from more than 150 nations. But all eyes were on the squad of girls from Afghanistan, who had twice travelled 800 kilometres to the American embassy in Kabul, only to have their visa applications turned down. They were finally granted entry with just one week to go until the event began after their story had gone viral. In an interview with Al Jazeera, before US officials decided to allow them in the country, team member Rodaba Noori said: "We wanted to show our talents to the world so they would know that we do have skills."

I've been at every one, from the tiny one with perhaps 100-200 people to this one with 1,400 that fills a large ballroom. Tuesday morning did not offer too many surprises. The first was an announcement by Toyota Research Institute of a $100M venture fund. Toyota committed $1B to this group a couple of years ago, but surprisingly Gil Pratt (who ran the DARPA Robotics Challenge for humanoid-like robots) has been somewhat a man of mixed views, with less optimistic forecasts. Different about this VC fund will be the use of DARPA like "calls."

Filters in convolutional networks are typically parameterized in a pixel basis, that does not take prior knowledge about the visual world into account. We investigate the generalized notion of frames designed with image properties in mind, as alternatives to this parametrization. We show that frame-based ResNets and Densenets can improve performance on Cifar-10+ consistently, while having additional pleasant properties like steerability. By exploiting these transformation properties explicitly, we arrive at dynamic steerable blocks. They are an extension of residual blocks, that are able to seamlessly transform filters under pre-defined transformations, conditioned on the input at training and inference time. Dynamic steerable blocks learn the degree of invariance from data and locally adapt filters, allowing them to apply a different geometrical variant of the same filter to each location of the feature map. When evaluated on the Berkeley Segmentation contour detection dataset, our approach outperforms all competing approaches that do not utilize pre-training. Our results highlight the benefits of image-based regularization to deep networks.

Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e.g., the dialogue success and the dialogue length. In this work, we propose a structured method for finding a good balance between these components by searching for the optimal reward component weighting. To render this search feasible, we use multi-objective reinforcement learning to significantly reduce the number of training dialogues required. We apply our proposed method to find optimized component weights for six domains and compare them to a default baseline.

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank has increased more than 15 fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence however is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D-convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The 2-layer architecture was investigated on a large dataset of 63,558 enzymes from the Protein Data Bank and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.

We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). The most relevant features for classification are selected using a bagging strategy. We evaluate the accuracy of our method in a group of 23 patients with AD (10 males, 13 females, age $\pm$ standard-deviation (SD) = 73 $\pm$ 6 years, mini-mental score (MMS) = 24.4 $\pm$ 2.8), 23 patients with amnestic MCI (10 males, 13 females, age $\pm$ SD = 74 $\pm$ 8 years, MMS = 27.3 $\pm$ 1.4) and 25 elderly healthy controls (13 males, 12 females, age $\pm$ SD = 64 $\pm$ 8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease.